Many computationally intensive financial simulation problems involve running the same simulation model repeatedly with different values of its inputs. Such tasks include pricing exotic options of the same type but of different strikes and maturities, valuation of options given different values of the model’s parameters during calibration, and measuring a portfolio’s risk as the markets move. The basic approach is to run the simulation model using each of the input values in which one is interested. In this talk, we explore generic methods for solving a suite of repeated simulation problems more efficiently, by estimating the answer given one value of the inputs using information generated while running the simulation model with different values of the inputs.